oddball: Spotting Anomalies in Weighted Graphs
نویسندگان
چکیده
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled “neighborhood sub-graphs” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is scalable and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition.
منابع مشابه
Anomaly Detection in Large Graphs
Discovering anomalies is an important and challenging task for many settings, from network intrusion to fraud detection. However, most work to date has focused on clouds of multi-dimensional points, with little emphasis on graph data; even then, the focus is on un-weighted, node-labeled graphs. Here we propose OddBall, an algorithm to detect anomalous nodes in weighted graphs. The contributions...
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